One of the advantages of robotic cleaners is that they can clean in places that are hard or impossible to clean manually. Many types of furnishings found in houses and commercial and industrial buildings provide a small amount of clearance under the furnishing. Examples include shelves, couches, and beds. These clearance spaces are often too small to clean easily manually, yet they are large enough to accumulate dirt and dust.
Previous attempts at providing robotic cleaners that can clean under furnishing have been limited in their flexibility, and also in their success. Most current commercial systems employ a “cleaning algorithms” technique. In this type of system, the cleaner moves in a single or a series of random or semi-random patterns around the floor. The pattern(s) are varied as too time, or other calculations the robot cleaner performs. However, in all cases, the goal of the cleaner is to optimize the cleaning of a larger space that happens to include the space under the furnishings.
These systems also have a design, such that the robot is able to fit into the very small under furnishing spaces, as well as passably clean the more open spaces of a building. The design compromises required to create a robotic cleaner that can both fit into the very small spaces under furnishings and also clean the open expanses of a building are daunting. Therefore, the efficiency and effectiveness of such systems is poor. For this reason, many systems have given up on this challenge entirely.
Efficiency and effectiveness of robot cleaners is typically low because the robots do not use techniques that make use of global location awareness. That is, they are generally unaware of their actual location. Previous service cleaning robots have ignored this problem, resulting in sub-optimal cleaning. Without a location context, the servicing robots are limited in terms of providing a series of tasks within an overall location, for example.
Moreover, current robot cleaners do not use what they learn as they do their job to improve their work. As an example, current robot cleaners will always clean when scheduled even when there is too much activity in the area to be able to clean at the scheduled time, resulting in a cleaning failure each time.
Also, that very action of servicing (e.g., cleaning) an environment or facility is a complex activity that can require a large fund of knowledge to perform adequately. Previous cleaning robots, as an example, have ignored this fund of knowledge problem. Instead, they tend to rely on generalized techniques to clean all different surfaces, objects, or functional areas of a facility.
Additionally, some service robots, such as robotic vacuums, can produce a relatively large amount of noise. Previous attempts at reducing noise in robotic cleaners have relied on traditional techniques that have been used to quiet manual vacuum cleaners. These include, but are not limited to: covering the working mechanisms with hard plastic shells, carefully designing moving parts to reduce noise production, and artificially limiting the strength of the cleaning mechanism to reduce the maximum noise levels products.